segmenter
segmentation_models.pytorch
| segmenter | segmentation_models.pytorch | |
|---|---|---|
| 1 | 14 | |
| 797 | 8,844 | |
| - | - | |
| 0.0 | 4.1 | |
| 15 days ago | 6 days ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
segmenter
-
Comparison of inference time between convolution nets and transformers
I am not very familiar with ViT (Transformer) based networks. But I tried https://github.com/rstrudel/segmenter to replace some CNN based segmentation nets.
segmentation_models.pytorch
-
Instance segmentation of small objects in grainy drone imagery
Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
-
[D] Improvements/alternatives to U-net for medical images segmentation?
SMP offers a wide variety of segmentation models with the option to use pre-trained weights.
-
Improvements/alternatives to U-net for medical images segmentation?
SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch
-
Medical Image Segmentation Human Retina
This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb
-
Automatic generation of image-segmentation mask pairs with StableDiffusion
Sounds like a good semantic segmentation problem, I like this repo: https://github.com/qubvel/segmentation_models.pytorch
-
Dice Score not decreasing when doing semantic segmentation
When i pass the CT-Scans and the masks to the Loss Function, which is the Jaccard-Loss from the segmentation_models.pytorch library, the value does not decrease but stay in the range of 1.0-0.9 over 50 epochs training on only one batch of 32 images. As far as I have understood, my network should overfit and the loss should decrease since I am only training on one batch of a small amount of images. However this does not happen. I also tried more batches with all the data over 100 epochs, but the loss does not decrease either obviously. Does anyone have an idea what I might have done wrong? Do I have to change anything when passing the masks to my loss function?
-
Good Brain Tumor segmentation model !?
I know there is a decent one in segmentation models python (MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation)
-
Advice needed
You could also use qubvel's segmentation models if you would like to explore semantic segmentation.
-
[D][R] Is there a standard architecture for U-Nets, pixel-to-pixel models, VAEs, and the like?
Check out segmentation models pytorch, really easy to use, has a great interface.
-
Pytorch GPU Memory Leak Problem: Cuda Out of Memory Error !!
Have you tried another implementation? For example: qubvel/segmentation_models.pytorch
What are some alternatives?
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
yolact - A simple, fully convolutional model for real-time instance segmentation.
HRNet-Semantic-Segmentation - The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch - Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images